Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations889
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.1 KiB
Average record size in memory128.0 B

Variable types

Numeric6
Categorical7
Text2

Alerts

FamilySize is highly overall correlated with Fare and 3 other fieldsHigh correlation
Fare is highly overall correlated with FamilySize and 1 other fieldsHigh correlation
Has_cabin is highly overall correlated with Fare and 1 other fieldsHigh correlation
IsAlone is highly overall correlated with FamilySize and 2 other fieldsHigh correlation
Parch is highly overall correlated with FamilySize and 1 other fieldsHigh correlation
Pclass is highly overall correlated with Has_cabinHigh correlation
Sex is highly overall correlated with Survived and 1 other fieldsHigh correlation
SibSp is highly overall correlated with FamilySize and 1 other fieldsHigh correlation
Survived is highly overall correlated with Sex and 1 other fieldsHigh correlation
Title is highly overall correlated with Sex and 1 other fieldsHigh correlation
PassengerId is uniformly distributed Uniform
PassengerId has unique values Unique
Name has unique values Unique
SibSp has 606 (68.2%) zeros Zeros
Parch has 676 (76.0%) zeros Zeros
Fare has 15 (1.7%) zeros Zeros

Reproduction

Analysis started2025-09-11 09:19:41.376652
Analysis finished2025-09-11 09:19:46.168128
Duration4.79 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct889
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2025-09-11T14:49:46.379092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.4
Q1224
median446
Q3668
95-th percentile846.6
Maximum891
Range890
Interquartile range (IQR)444

Descriptive statistics

Standard deviation256.99817
Coefficient of variation (CV)0.57622909
Kurtosis-1.1971564
Mean446
Median Absolute Deviation (MAD)222
Skewness0
Sum396494
Variance66048.061
MonotonicityStrictly increasing
2025-09-11T14:49:46.527134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
891 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
Other values (879) 879
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
0
549 
1
340 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters889
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 549
61.8%
1 340
38.2%

Length

2025-09-11T14:49:46.660491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-11T14:49:46.760376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 549
61.8%
1 340
38.2%

Most occurring characters

ValueCountFrequency (%)
0 549
61.8%
1 340
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 549
61.8%
1 340
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 549
61.8%
1 340
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 549
61.8%
1 340
38.2%

Pclass
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
3
491 
1
214 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters889
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 491
55.2%
1 214
24.1%
2 184
 
20.7%

Length

2025-09-11T14:49:46.860285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-11T14:49:46.953224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 491
55.2%
1 214
24.1%
2 184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3 491
55.2%
1 214
24.1%
2 184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 491
55.2%
1 214
24.1%
2 184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 491
55.2%
1 214
24.1%
2 184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 491
55.2%
1 214
24.1%
2 184
 
20.7%

Name
Text

Unique 

Distinct889
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2025-09-11T14:49:47.176970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length82
Median length52
Mean length26.95838
Min length12

Characters and Unicode

Total characters23966
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique889 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr 521
 
14.4%
miss 181
 
5.0%
mrs 128
 
3.5%
william 64
 
1.8%
john 44
 
1.2%
master 40
 
1.1%
henry 35
 
1.0%
james 24
 
0.7%
george 23
 
0.6%
charles 23
 
0.6%
Other values (1512) 2532
70.0%
2025-09-11T14:49:47.579152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2728
 
11.4%
r 1954
 
8.2%
e 1696
 
7.1%
a 1654
 
6.9%
i 1323
 
5.5%
n 1301
 
5.4%
s 1293
 
5.4%
M 1125
 
4.7%
l 1064
 
4.4%
o 1005
 
4.2%
Other values (50) 8823
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2728
 
11.4%
r 1954
 
8.2%
e 1696
 
7.1%
a 1654
 
6.9%
i 1323
 
5.5%
n 1301
 
5.4%
s 1293
 
5.4%
M 1125
 
4.7%
l 1064
 
4.4%
o 1005
 
4.2%
Other values (50) 8823
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2728
 
11.4%
r 1954
 
8.2%
e 1696
 
7.1%
a 1654
 
6.9%
i 1323
 
5.5%
n 1301
 
5.4%
s 1293
 
5.4%
M 1125
 
4.7%
l 1064
 
4.4%
o 1005
 
4.2%
Other values (50) 8823
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2728
 
11.4%
r 1954
 
8.2%
e 1696
 
7.1%
a 1654
 
6.9%
i 1323
 
5.5%
n 1301
 
5.4%
s 1293
 
5.4%
M 1125
 
4.7%
l 1064
 
4.4%
o 1005
 
4.2%
Other values (50) 8823
36.8%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
male
577 
female
312 

Length

Max length6
Median length4
Mean length4.7019123
Min length4

Characters and Unicode

Total characters4180
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 577
64.9%
female 312
35.1%

Length

2025-09-11T14:49:47.710439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-11T14:49:47.809980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 577
64.9%
female 312
35.1%

Most occurring characters

ValueCountFrequency (%)
e 1201
28.7%
m 889
21.3%
a 889
21.3%
l 889
21.3%
f 312
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1201
28.7%
m 889
21.3%
a 889
21.3%
l 889
21.3%
f 312
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1201
28.7%
m 889
21.3%
a 889
21.3%
l 889
21.3%
f 312
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1201
28.7%
m 889
21.3%
a 889
21.3%
l 889
21.3%
f 312
 
7.5%

Age
Real number (ℝ)

Distinct88
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.315152
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2025-09-11T14:49:47.910537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile6
Q122
median28
Q335
95-th percentile54
Maximum80
Range79.58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.984932
Coefficient of variation (CV)0.44294269
Kurtosis1.0078198
Mean29.315152
Median Absolute Deviation (MAD)6
Skewness0.50801008
Sum26061.17
Variance168.60847
MonotonicityNot monotonic
2025-09-11T14:49:48.012317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 202
22.7%
24 30
 
3.4%
22 27
 
3.0%
18 26
 
2.9%
30 25
 
2.8%
19 25
 
2.8%
21 24
 
2.7%
25 23
 
2.6%
36 22
 
2.5%
29 20
 
2.2%
Other values (78) 465
52.3%
ValueCountFrequency (%)
0.42 1
 
0.1%
0.67 1
 
0.1%
0.75 2
 
0.2%
0.83 2
 
0.2%
0.92 1
 
0.1%
1 7
0.8%
2 10
1.1%
3 6
0.7%
4 10
1.1%
5 4
 
0.4%
ValueCountFrequency (%)
80 1
 
0.1%
74 1
 
0.1%
71 2
0.2%
70.5 1
 
0.1%
70 2
0.2%
66 1
 
0.1%
65 3
0.3%
64 2
0.2%
63 2
0.2%
62 3
0.3%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52418448
Minimum0
Maximum8
Zeros606
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2025-09-11T14:49:48.114145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1037049
Coefficient of variation (CV)2.1055657
Kurtosis17.838972
Mean0.52418448
Median Absolute Deviation (MAD)0
Skewness3.6910576
Sum466
Variance1.2181645
MonotonicityNot monotonic
2025-09-11T14:49:48.210085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 606
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
ValueCountFrequency (%)
0 606
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
ValueCountFrequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 606
68.2%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38245219
Minimum0
Maximum6
Zeros676
Zeros (%)76.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2025-09-11T14:49:48.303416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80676074
Coefficient of variation (CV)2.1094421
Kurtosis9.7505917
Mean0.38245219
Median Absolute Deviation (MAD)0
Skewness2.7451601
Sum340
Variance0.6508629
MonotonicityNot monotonic
2025-09-11T14:49:48.381183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 676
76.0%
1 118
 
13.3%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
ValueCountFrequency (%)
0 676
76.0%
1 118
 
13.3%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.3%
0 676
76.0%

Ticket
Text

Distinct680
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2025-09-11T14:49:48.605516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.7525309
Min length3

Characters and Unicode

Total characters6003
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)61.5%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc 60
 
5.3%
c.a 27
 
2.4%
a/5 17
 
1.5%
ca 14
 
1.2%
2 12
 
1.1%
ston/o 12
 
1.1%
w./c 9
 
0.8%
sc/paris 9
 
0.8%
soton/o.q 8
 
0.7%
2343 7
 
0.6%
Other values (708) 953
84.5%
2025-09-11T14:49:48.960400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 744
12.4%
1 685
11.4%
2 592
9.9%
7 488
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.8%
5 385
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 744
12.4%
1 685
11.4%
2 592
9.9%
7 488
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.8%
5 385
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 744
12.4%
1 685
11.4%
2 592
9.9%
7 488
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.8%
5 385
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 744
12.4%
1 685
11.4%
2 592
9.9%
7 488
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.8%
5 385
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Fare
Real number (ℝ)

High correlation  Zeros 

Distinct247
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.096681
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2025-09-11T14:49:49.109921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.8958
median14.4542
Q331
95-th percentile112.31832
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.1042

Descriptive statistics

Standard deviation49.697504
Coefficient of variation (CV)1.548369
Kurtosis33.508477
Mean32.096681
Median Absolute Deviation (MAD)6.9042
Skewness4.8014402
Sum28533.949
Variance2469.8419
MonotonicityNot monotonic
2025-09-11T14:49:49.227056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.05 43
 
4.8%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
0 15
 
1.7%
Other values (237) 613
69.0%
ValueCountFrequency (%)
0 15
1.7%
4.0125 1
 
0.1%
5 1
 
0.1%
6.2375 1
 
0.1%
6.4375 1
 
0.1%
6.45 1
 
0.1%
6.4958 2
 
0.2%
6.75 2
 
0.2%
6.8583 1
 
0.1%
6.95 1
 
0.1%
ValueCountFrequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Embarked
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
S
644 
C
168 
Q
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters889
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Length

2025-09-11T14:49:49.352401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-11T14:49:49.460343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
s 644
72.4%
c 168
 
18.9%
q 77
 
8.7%

Most occurring characters

ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 644
72.4%
C 168
 
18.9%
Q 77
 
8.7%

Has_cabin
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
0
687 
1
202 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters889
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 687
77.3%
1 202
 
22.7%

Length

2025-09-11T14:49:49.763413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-11T14:49:49.864304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 687
77.3%
1 202
 
22.7%

Most occurring characters

ValueCountFrequency (%)
0 687
77.3%
1 202
 
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 687
77.3%
1 202
 
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 687
77.3%
1 202
 
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 687
77.3%
1 202
 
22.7%

FamilySize
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9066367
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2025-09-11T14:49:49.943320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6147026
Coefficient of variation (CV)0.84688531
Kurtosis9.1356639
Mean1.9066367
Median Absolute Deviation (MAD)0
Skewness2.7238921
Sum1695
Variance2.6072645
MonotonicityNot monotonic
2025-09-11T14:49:50.026715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 535
60.2%
2 161
 
18.1%
3 102
 
11.5%
4 29
 
3.3%
6 22
 
2.5%
5 15
 
1.7%
7 12
 
1.3%
11 7
 
0.8%
8 6
 
0.7%
ValueCountFrequency (%)
1 535
60.2%
2 161
 
18.1%
3 102
 
11.5%
4 29
 
3.3%
5 15
 
1.7%
6 22
 
2.5%
7 12
 
1.3%
8 6
 
0.7%
11 7
 
0.8%
ValueCountFrequency (%)
11 7
 
0.8%
8 6
 
0.7%
7 12
 
1.3%
6 22
 
2.5%
5 15
 
1.7%
4 29
 
3.3%
3 102
 
11.5%
2 161
 
18.1%
1 535
60.2%

IsAlone
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
1
535 
0
354 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters889
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 535
60.2%
0 354
39.8%

Length

2025-09-11T14:49:50.158813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-11T14:49:50.245652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 535
60.2%
0 354
39.8%

Most occurring characters

ValueCountFrequency (%)
1 535
60.2%
0 354
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 535
60.2%
0 354
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 535
60.2%
0 354
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 535
60.2%
0 354
39.8%

Title
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
Mr
517 
Miss
184 
Mrs
125 
Rare
63 

Length

Max length4
Median length2
Mean length2.696288
Min length2

Characters and Unicode

Total characters2397
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMr
2nd rowMrs
3rd rowMiss
4th rowMrs
5th rowMr

Common Values

ValueCountFrequency (%)
Mr 517
58.2%
Miss 184
 
20.7%
Mrs 125
 
14.1%
Rare 63
 
7.1%

Length

2025-09-11T14:49:50.356461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-11T14:49:50.485927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mr 517
58.2%
miss 184
 
20.7%
mrs 125
 
14.1%
rare 63
 
7.1%

Most occurring characters

ValueCountFrequency (%)
M 826
34.5%
r 705
29.4%
s 493
20.6%
i 184
 
7.7%
R 63
 
2.6%
a 63
 
2.6%
e 63
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 826
34.5%
r 705
29.4%
s 493
20.6%
i 184
 
7.7%
R 63
 
2.6%
a 63
 
2.6%
e 63
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 826
34.5%
r 705
29.4%
s 493
20.6%
i 184
 
7.7%
R 63
 
2.6%
a 63
 
2.6%
e 63
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 826
34.5%
r 705
29.4%
s 493
20.6%
i 184
 
7.7%
R 63
 
2.6%
a 63
 
2.6%
e 63
 
2.6%

Interactions

2025-09-11T14:49:45.141331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.085320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.601313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:43.205178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:43.905559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:44.498512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:45.240979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.180931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.679132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:43.293587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:43.985611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:44.593734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:45.324740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.268190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.781355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:43.407683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:44.079978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:44.705469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:45.450474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.354069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.879925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:43.493370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:44.204998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:44.814111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:45.542121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.439842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.959927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:43.587571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:44.312491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:44.938411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:45.647188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:42.517372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:43.116249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:43.821883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:44.407947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-11T14:49:45.047316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-09-11T14:49:50.559896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeEmbarkedFamilySizeFareHas_cabinIsAloneParchPassengerIdPclassSexSibSpSurvivedTitle
Age1.0000.150-0.1810.1220.2720.348-0.2170.0340.2620.107-0.1440.1580.338
Embarked0.1501.0000.0830.1960.2280.1100.0520.0000.2600.1130.0920.1660.131
FamilySize-0.1810.0831.0000.5330.0700.6420.801-0.0510.1370.2070.8490.2160.244
Fare0.1220.1960.5331.0000.5780.3100.413-0.0140.4770.1850.4510.2790.113
Has_cabin0.2720.2280.0700.5781.0000.1560.0940.0690.7880.1290.1400.3090.132
IsAlone0.3480.1100.6420.3100.1561.0000.6860.0130.1310.3030.8370.2010.465
Parch-0.2170.0520.8010.4130.0940.6861.0000.0010.0220.2490.4500.1590.272
PassengerId0.0340.000-0.051-0.0140.0690.0130.0011.0000.0370.064-0.0610.1040.024
Pclass0.2620.2600.1370.4770.7880.1310.0220.0371.0000.1260.1490.3340.133
Sex0.1070.1130.2070.1850.1290.3030.2490.0640.1261.0000.2090.5380.992
SibSp-0.1440.0920.8490.4510.1400.8370.450-0.0610.1490.2091.0000.1890.296
Survived0.1580.1660.2160.2790.3090.2010.1590.1040.3340.5380.1891.0000.561
Title0.3380.1310.2440.1130.1320.4650.2720.0240.1330.9920.2960.5611.000

Missing values

2025-09-11T14:49:45.792446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-11T14:49:46.048410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarkedHas_cabinFamilySizeIsAloneTitle
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500S020Mr
1211Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C120Mrs
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250S011Miss
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000S120Mrs
4503Allen, Mr. William Henrymale35.0003734508.0500S011Mr
5603Moran, Mr. Jamesmale28.0003308778.4583Q011Mr
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625S111Mr
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750S050Rare
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333S030Mrs
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708C020Mrs
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarkedHas_cabinFamilySizeIsAloneTitle
88188203Markun, Mr. Johannmale33.0003492577.8958S011Mr
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167S011Miss
88388402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000S011Mr
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500S011Mr
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250Q060Mrs
88688702Montvila, Rev. Juozasmale27.00021153613.0000S011Rare
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000S111Miss
88888903Johnston, Miss. Catherine Helen "Carrie"female28.012W./C. 660723.4500S040Miss
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C111Mr
89089103Dooley, Mr. Patrickmale32.0003703767.7500Q011Mr